Governance of Automated Decisions: Audit, Oversight and Control for AI Decision Models Training Course

Transformations When Decisions Become Model-Driven

• Transitioning from rule-based logic and human judgment to scored options.
• The velocity and scale of automated actions.
• Emerging failure modes and accountability voids.

Decision Inventory and Risk Tiering

• Creating a catalog of automated decisions.
• Analyzing impact, reversibility, and affected stakeholders.
• Defining risk tiers and corresponding control expectations.
• Assigning ownership for each decision class.

Accountability and Human Supervision

• Distinguishing between meaningful and nominal human review.
• Treating autonomy thresholds as governance decisions.
• Defining escalation pathways and response timelines.
• Identifying the accountable owner and sign-off process.

Decision Logging and Explainability

• Components of a defensible decision record.
• Retention of confidence scores, options, and context.
• Traceability of models and versions.
• Explaining decisions to customers or regulatory bodies.

Policy and Boundary Management

• Approved option sets and change control processes.
• Governance of thresholds and periodic reviews.
• Prohibited decisions and hard stops.
• Implementing policy as code alongside model output.

Regulatory Requirements

• Obligations under the EU AI Act for decision systems.
• Sector-specific regulations in finance, insurance, and health.
• Data protection standards and rights regarding automated decisions.
• Documentation and evidence of conformity.

Vendor, Licensing, and Provenance Risks

• Hosted decision APIs versus open-weight models.
• Licensing terms for derivative and embedded models.
• Supplier dependency and discontinuation risks.
• Weight integrity and version pinning.

Bias, Fairness, and Outcome Surveillance

• Outcome disparities across different groups.
• Proxy variables and the framing of options.
• Signals from appeals and complaints.
• Regular fairness assessments.

Incident Response for Erroneous Decisions

• Identifying systematic decision errors.
• Containing issues and suspending autonomy.
• Remediation measures and customer redress.
• Root cause analysis and control updates.

Assurance Review of Decision Layers

• Evidence typically required by auditors.
• Testing claims regarding accuracy and calibration.
• Sampling decisions and overrides.
• Reporting findings to the board of directors.